import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

print(tf.__version__)

#load dataset
mnist=keras.datasets.mnist
(train_images,train_labels),(test_images,test_labels)=mnist.load_data()

#set labels
class_names=[0,1,2,3,4,5,6,7,8,9]

#print shapes of training set and test set
print(train_images.shape)
print(train_labels.shape)
print(test_images.shape)
print(test_labels.shape)
   

#normalize
train_images=train_images/255.0
test_images=test_images/255.0

#display the first 16 images
plt.figure(figsize=(10,10))
for i in range(16):
	plt.subplot(4,4,i+1)
	plt.xticks([])
	plt.yticks([])
	plt.grid(False)
	plt.imshow(train_images[i],cmap=plt.cm.binary)
	plt.xlabel(train_labels[i])
plt.show()


#create model
model=keras.Sequential([
	keras.layers.Flatten(input_shape=(28,28)),
	keras.layers.Dense(128,activation='relu'),
	keras.layers.Dense(10)
])

#compile model
model.compile(optimizer='adam',
	      loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

#train model
model.fit(train_images,train_labels,epochs=10,batch_size=64)

#evaluate accuracy 
test_loss,test_acc=model.evaluate(test_images,test_labels,verbose=2)
print('\nTest accuracy',test_acc)

#predict
probability_model=tf.keras.Sequential([model,tf.keras.layers.Softmax()])
predictions=probability_model.predict(test_images)

#predict the first image
print(predictions[0])

#visualize the 10 prediction classes
#draw images
def plot_image(i,predictions_array,true_label,img):
	predictions_array,true_label,img=predictions_array,true_label[i],img[i]
	plt.grid(False)
	plt.xticks([])
	plt.yticks([])
	plt.imshow(img,cmap=plt.cm.binary)
	
	predicted_label=np.argmax(predictions_array)
	if predicted_label==true_label:
		color='blue'
	else:
		color='red'
	plt.xlabel("{}{:2.0f}%({})".format(predicted_label,
					   100*np.max(predictions_array),
					   true_label),
				           color=color)

#draw predicted value	
def plot_value_array(i,predictions_array,true_label):
	predictions_array,true_label=predictions_array,true_label[i]
	plt.grid(False)
	plt.xticks(range(10))
	plt.yticks([])
	thisplot=plt.bar(range(10),predictions_array,color="#777777")
	plt.ylim([0,1])
	predicted_label=np.argmax(predictions_array)

	thisplot[predicted_label].set_color('red')
	thisplot[true_label].set_color('blue')


#draw a test image and its predicted label and groud truth
num_rows=5
num_cols=3
num_images=num_rows*num_cols
plt.figure(figsize=(2*2*num_cols,2*num_rows))
for i in range(num_images):
	plt.subplot(num_rows,2*num_cols,2*i+1)
	plot_image(i,predictions[i],test_labels,test_images)
	plt.subplot(num_rows,2*num_cols,2*i+2)
	plot_value_array(i,predictions[i],test_labels)
plt.tight_layout()
plt.show()

#use trained model to capture the image form the test datset
img=test_images[1]
print(img.shape)
#visualize the first image
plt.figure()
plt.imshow(img)
plt.colorbar()
plt.grid(False)
plt.show()

#add the image to the batprocessing list
img=(np.expand_dims(img,0))
print(img.shape)

#predict the labels
predictions_single=probability_model.predict(img)
print(predictions_single)
plot_value_array(1,predictions_single[0],test_labels)
_=plt.xticks(range(10),class_names,rotation=45)
plt.show()

#predictions
print(np.argmax(predictions_single[0]))

